Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations7447
Missing cells32973
Missing cells (%)49.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory291.0 KiB
Average record size in memory40.0 B

Variable types

Numeric9

Alerts

Time is highly overall correlated with root.sg1.device1.projetv7.A and 4 other fieldsHigh correlation
root.sg1.device1.projetv7.A is highly overall correlated with Time and 4 other fieldsHigh correlation
root.sg1.device1.projetv7.C is highly overall correlated with root.sg1.device1.projetv7.A and 4 other fieldsHigh correlation
root.sg1.device1.projetv7.D is highly overall correlated with Time and 5 other fieldsHigh correlation
root.sg1.device1.projetv7.E is highly overall correlated with root.sg1.device1.projetv7.CHigh correlation
root.sg1.device1.projetv7.F is highly overall correlated with Time and 3 other fieldsHigh correlation
root.sg1.device1.projetv7.G is highly overall correlated with Time and 5 other fieldsHigh correlation
root.sg1.device1.projetv7.OT is highly overall correlated with Time and 5 other fieldsHigh correlation
root.sg1.device1.projetv7.A has 4116 (55.3%) missing values Missing
root.sg1.device1.projetv7.B has 4134 (55.5%) missing values Missing
root.sg1.device1.projetv7.C has 4120 (55.3%) missing values Missing
root.sg1.device1.projetv7.D has 4121 (55.3%) missing values Missing
root.sg1.device1.projetv7.OT has 4190 (56.3%) missing values Missing
root.sg1.device1.projetv7.E has 3981 (53.5%) missing values Missing
root.sg1.device1.projetv7.F has 4055 (54.5%) missing values Missing
root.sg1.device1.projetv7.G has 4256 (57.2%) missing values Missing
Time is uniformly distributed Uniform
Time has unique values Unique

Reproduction

Analysis started2025-01-09 12:43:56.783539
Analysis finished2025-01-09 12:44:06.304770
Duration9.52 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Time
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct7447
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5459963 × 1011
Minimum6.311484 × 1011
Maximum1.2780216 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.3 KiB
2025-01-09T13:44:06.530418image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum6.311484 × 1011
5-th percentile6.6331512 × 1011
Q17.922412 × 1011
median9.548856 × 1011
Q31.1170152 × 1012
95-th percentile1.2457685 × 1012
Maximum1.2780216 × 1012
Range6.468732 × 1011
Interquartile range (IQR)3.24774 × 1011

Descriptive statistics

Standard deviation1.8711028 × 1011
Coefficient of variation (CV)0.19600916
Kurtosis-1.2049756
Mean9.5459963 × 1011
Median Absolute Deviation (MAD)1.624284 × 1011
Skewness-0.00048027435
Sum7.1089034 × 1015
Variance3.5010255 × 1022
MonotonicityStrictly increasing
2025-01-09T13:44:06.735488image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2780216 × 10121
 
< 0.1%
6.311484 × 10111
 
< 0.1%
6.312348 × 10111
 
< 0.1%
6.313212 × 10111
 
< 0.1%
6.314076 × 10111
 
< 0.1%
6.31494 × 10111
 
< 0.1%
6.315804 × 10111
 
< 0.1%
1.2766392 × 10121
 
< 0.1%
1.2765528 × 10121
 
< 0.1%
1.2764664 × 10121
 
< 0.1%
Other values (7437) 7437
99.9%
ValueCountFrequency (%)
6.311484 × 10111
< 0.1%
6.312348 × 10111
< 0.1%
6.313212 × 10111
< 0.1%
6.314076 × 10111
< 0.1%
6.31494 × 10111
< 0.1%
6.315804 × 10111
< 0.1%
6.316668 × 10111
< 0.1%
6.317532 × 10111
< 0.1%
6.318396 × 10111
< 0.1%
6.31926 × 10111
< 0.1%
ValueCountFrequency (%)
1.2780216 × 10121
< 0.1%
1.2779352 × 10121
< 0.1%
1.2778488 × 10121
< 0.1%
1.2777624 × 10121
< 0.1%
1.277676 × 10121
< 0.1%
1.2775896 × 10121
< 0.1%
1.2775032 × 10121
< 0.1%
1.2774168 × 10121
< 0.1%
1.2773304 × 10121
< 0.1%
1.277244 × 10121
< 0.1%

root.sg1.device1.projetv7.A
Real number (ℝ)

High correlation  Missing 

Distinct2670
Distinct (%)80.2%
Missing4116
Missing (%)55.3%
Infinite0
Infinite (%)0.0%
Mean0.78100636
Minimum0.48474199
Maximum1.102536
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.2 KiB
2025-01-09T13:44:06.835265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.48474199
5-th percentile0.5818955
Q10.69840002
median0.76295
Q30.87813652
95-th percentile1.0259725
Maximum1.102536
Range0.61779398
Interquartile range (IQR)0.1797365

Descriptive statistics

Standard deviation0.12952192
Coefficient of variation (CV)0.16583978
Kurtosis-0.41181025
Mean0.78100636
Median Absolute Deviation (MAD)0.08464998
Skewness0.28831139
Sum2601.5322
Variance0.016775927
MonotonicityNot monotonic
2025-01-09T13:44:06.931763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6700000167 6
 
0.1%
0.7839999795 5
 
0.1%
0.7785000205 5
 
0.1%
0.7904999852 5
 
0.1%
0.7524999976 5
 
0.1%
0.7164999843 5
 
0.1%
0.78549999 5
 
0.1%
0.7404999733 4
 
0.1%
0.7409999967 4
 
0.1%
0.7400000095 4
 
0.1%
Other values (2660) 3283
44.1%
(Missing) 4116
55.3%
ValueCountFrequency (%)
0.4847419858 1
< 0.1%
0.4907999933 1
< 0.1%
0.4915989935 1
< 0.1%
0.4919959903 1
< 0.1%
0.4920969903 1
< 0.1%
0.4928489923 1
< 0.1%
0.4939979911 1
< 0.1%
0.4951980114 1
< 0.1%
0.4975500107 1
< 0.1%
0.4993000031 1
< 0.1%
ValueCountFrequency (%)
1.102535963 1
< 0.1%
1.101698041 1
< 0.1%
1.099625945 1
< 0.1%
1.096467018 1
< 0.1%
1.080450058 1
< 0.1%
1.079563975 1
< 0.1%
1.078480959 1
< 0.1%
1.077993035 1
< 0.1%
1.077528 1
< 0.1%
1.077353954 1
< 0.1%

root.sg1.device1.projetv7.B
Real number (ℝ)

Missing 

Distinct2789
Distinct (%)84.2%
Missing4134
Missing (%)55.5%
Infinite0
Infinite (%)0.0%
Mean1.639203
Minimum1.28817
Maximum2.109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.2 KiB
2025-01-09T13:44:07.026960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1.28817
5-th percentile1.42738
Q11.541545
median1.6081049
Q31.703737
95-th percentile1.95217
Maximum2.109
Range0.82082999
Interquartile range (IQR)0.16219199

Descriptive statistics

Standard deviation0.1590191
Coefficient of variation (CV)0.09701001
Kurtosis0.021711931
Mean1.639203
Median Absolute Deviation (MAD)0.075195074
Skewness0.65998381
Sum5430.6795
Variance0.025287071
MonotonicityNot monotonic
2025-01-09T13:44:07.119164image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.638499975 5
 
0.1%
1.641499996 5
 
0.1%
1.586500049 5
 
0.1%
1.611395955 5
 
0.1%
1.562999964 5
 
0.1%
1.568874002 4
 
0.1%
1.672000051 4
 
0.1%
1.684000015 4
 
0.1%
1.60246098 4
 
0.1%
1.636000037 4
 
0.1%
Other values (2779) 3268
43.9%
(Missing) 4134
55.5%
ValueCountFrequency (%)
1.28816998 1
< 0.1%
1.291046977 1
< 0.1%
1.291872978 2
< 0.1%
1.294749975 1
< 0.1%
1.295295 1
< 0.1%
1.297353029 1
< 0.1%
1.297597051 1
< 0.1%
1.29954505 1
< 0.1%
1.300001025 1
< 0.1%
1.300102949 1
< 0.1%
ValueCountFrequency (%)
2.108999968 1
< 0.1%
2.104099989 1
< 0.1%
2.08920002 1
< 0.1%
2.086999893 1
< 0.1%
2.085200071 1
< 0.1%
2.08100009 1
< 0.1%
2.079999924 1
< 0.1%
2.076100111 1
< 0.1%
2.072799921 1
< 0.1%
2.070300102 1
< 0.1%

root.sg1.device1.projetv7.C
Real number (ℝ)

High correlation  Missing 

Distinct2737
Distinct (%)82.3%
Missing4120
Missing (%)55.3%
Infinite0
Infinite (%)0.0%
Mean0.81441581
Minimum0.62015498
Maximum1.06076
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.2 KiB
2025-01-09T13:44:07.212802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.62015498
5-th percentile0.6466567
Q10.72437501
median0.79806203
Q30.89809549
95-th percentile1.006529
Maximum1.06076
Range0.44060504
Interquartile range (IQR)0.17372048

Descriptive statistics

Standard deviation0.11510801
Coefficient of variation (CV)0.14133814
Kurtosis-1.0816177
Mean0.81441581
Median Absolute Deviation (MAD)0.08198601
Skewness0.2591677
Sum2709.5614
Variance0.013249855
MonotonicityNot monotonic
2025-01-09T13:44:07.308717image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9871670008 6
 
0.1%
0.7535799742 5
 
0.1%
0.7366480231 5
 
0.1%
0.7316889763 4
 
0.1%
0.7237460017 4
 
0.1%
0.7222819924 4
 
0.1%
0.7293949723 4
 
0.1%
0.7371910214 4
 
0.1%
0.7367569804 4
 
0.1%
0.7331380248 4
 
0.1%
Other values (2727) 3283
44.1%
(Missing) 4120
55.3%
ValueCountFrequency (%)
0.6201549768 1
< 0.1%
0.6242200136 1
< 0.1%
0.6244930029 2
< 0.1%
0.6248049736 1
< 0.1%
0.6249219775 1
< 0.1%
0.6251170039 1
< 0.1%
0.6254889965 1
< 0.1%
0.6258999705 1
< 0.1%
0.62668401 1
< 0.1%
0.6267229915 1
< 0.1%
ValueCountFrequency (%)
1.060760021 1
< 0.1%
1.058290958 1
< 0.1%
1.058066964 1
< 0.1%
1.054407001 2
< 0.1%
1.053951979 1
< 0.1%
1.053629994 1
< 0.1%
1.053297043 1
< 0.1%
1.047011018 2
< 0.1%
1.046123981 1
< 0.1%
1.046079993 1
< 0.1%

root.sg1.device1.projetv7.D
Real number (ℝ)

High correlation  Missing 

Distinct2761
Distinct (%)83.0%
Missing4121
Missing (%)55.3%
Infinite0
Infinite (%)0.0%
Mean0.83514811
Minimum0.549739
Maximum1.279541
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.2 KiB
2025-01-09T13:44:07.401115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.549739
5-th percentile0.59670874
Q10.68558426
median0.801539
Q30.98886076
95-th percentile1.1176695
Maximum1.279541
Range0.72980201
Interquartile range (IQR)0.30327649

Descriptive statistics

Standard deviation0.17188548
Coefficient of variation (CV)0.20581436
Kurtosis-1.0577641
Mean0.83514811
Median Absolute Deviation (MAD)0.13509399
Skewness0.37232205
Sum2777.7026
Variance0.029544614
MonotonicityNot monotonic
2025-01-09T13:44:07.500791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6854010224 9
 
0.1%
0.6565989852 5
 
0.1%
0.6600660086 5
 
0.1%
0.6631299853 5
 
0.1%
0.6870489717 4
 
0.1%
0.7077140212 4
 
0.1%
0.6713659763 4
 
0.1%
0.6788870096 4
 
0.1%
0.7171030045 4
 
0.1%
0.6738539934 4
 
0.1%
Other values (2751) 3278
44.0%
(Missing) 4121
55.3%
ValueCountFrequency (%)
0.5497390032 1
< 0.1%
0.5540869832 1
< 0.1%
0.5545420051 1
< 0.1%
0.5551919937 1
< 0.1%
0.5556390285 1
< 0.1%
0.556129992 1
< 0.1%
0.5567770004 1
< 0.1%
0.5570409894 1
< 0.1%
0.5571029782 1
< 0.1%
0.5571249723 1
< 0.1%
ValueCountFrequency (%)
1.279541016 1
< 0.1%
1.274664998 1
< 0.1%
1.272328973 1
< 0.1%
1.272119045 1
< 0.1%
1.267426968 1
< 0.1%
1.248438954 1
< 0.1%
1.246152997 1
< 0.1%
1.245439053 1
< 0.1%
1.240941048 1
< 0.1%
1.231497049 1
< 0.1%

root.sg1.device1.projetv7.OT
Real number (ℝ)

High correlation  Missing 

Distinct2585
Distinct (%)79.4%
Missing4190
Missing (%)56.3%
Infinite0
Infinite (%)0.0%
Mean0.6436474
Minimum0.39315301
Maximum0.87750101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.2 KiB
2025-01-09T13:44:07.602918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.39315301
5-th percentile0.42868
Q10.55465001
median0.65249997
Q30.71658897
95-th percentile0.83713502
Maximum0.87750101
Range0.484348
Interquartile range (IQR)0.16193897

Descriptive statistics

Standard deviation0.11778555
Coefficient of variation (CV)0.18299701
Kurtosis-0.66345024
Mean0.6436474
Median Absolute Deviation (MAD)0.078699946
Skewness-0.10006496
Sum2096.3596
Variance0.013873436
MonotonicityNot monotonic
2025-01-09T13:44:07.705992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5734999776 7
 
0.1%
0.587199986 5
 
0.1%
0.6762999892 5
 
0.1%
0.5755000114 5
 
0.1%
0.5749999881 5
 
0.1%
0.6744999886 5
 
0.1%
0.5917999744 5
 
0.1%
0.8274379969 5
 
0.1%
0.5450000167 5
 
0.1%
0.5770000219 5
 
0.1%
Other values (2575) 3205
43.0%
(Missing) 4190
56.3%
ValueCountFrequency (%)
0.3931530118 1
< 0.1%
0.3940109909 1
< 0.1%
0.3943609893 1
< 0.1%
0.3946999907 1
< 0.1%
0.3948499858 1
< 0.1%
0.3958309889 1
< 0.1%
0.3964909911 1
< 0.1%
0.3968499899 1
< 0.1%
0.3972010016 1
< 0.1%
0.3975909948 1
< 0.1%
ValueCountFrequency (%)
0.8775010109 1
< 0.1%
0.8772699833 2
< 0.1%
0.8765010238 1
< 0.1%
0.8743550181 1
< 0.1%
0.8715360165 1
< 0.1%
0.8714600205 1
< 0.1%
0.8700190187 1
< 0.1%
0.8696789742 1
< 0.1%
0.8691499829 1
< 0.1%
0.8690360188 1
< 0.1%

root.sg1.device1.projetv7.E
Real number (ℝ)

High correlation  Missing 

Distinct1616
Distinct (%)46.6%
Missing3981
Missing (%)53.5%
Infinite0
Infinite (%)0.0%
Mean0.14177039
Minimum0.109292
Maximum0.21124201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.2 KiB
2025-01-09T13:44:07.806590image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.109292
5-th percentile0.119758
Q10.120809
median0.13508449
Q30.159895
95-th percentile0.185377
Maximum0.21124201
Range0.10195
Interquartile range (IQR)0.039085999

Descriptive statistics

Standard deviation0.02386459
Coefficient of variation (CV)0.16833268
Kurtosis-0.028691309
Mean0.14177039
Median Absolute Deviation (MAD)0.014851995
Skewness0.84899944
Sum491.37616
Variance0.00056951866
MonotonicityNot monotonic
2025-01-09T13:44:07.906425image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1208240017 144
 
1.9%
0.120337002 88
 
1.2%
0.2112420052 82
 
1.1%
0.1208150014 63
 
0.8%
0.1208200008 43
 
0.6%
0.1208230034 41
 
0.6%
0.1208209991 40
 
0.5%
0.1208179966 37
 
0.5%
0.1208169982 33
 
0.4%
0.1910150051 32
 
0.4%
Other values (1606) 2863
38.4%
(Missing) 3981
53.5%
ValueCountFrequency (%)
0.1092920005 1
 
< 0.1%
0.1144189984 1
 
< 0.1%
0.1145239994 1
 
< 0.1%
0.1145490035 1
 
< 0.1%
0.1145579964 5
0.1%
0.1145690009 1
 
< 0.1%
0.1145699993 1
 
< 0.1%
0.1145740002 1
 
< 0.1%
0.114575997 1
 
< 0.1%
0.1145899966 2
 
< 0.1%
ValueCountFrequency (%)
0.2112420052 82
1.1%
0.1910150051 32
 
0.4%
0.1892970055 4
 
0.1%
0.1892040074 3
 
< 0.1%
0.189071998 1
 
< 0.1%
0.1887540072 1
 
< 0.1%
0.1886540055 3
 
< 0.1%
0.1885509938 5
 
0.1%
0.1883839965 1
 
< 0.1%
0.187966004 6
 
0.1%

root.sg1.device1.projetv7.F
Real number (ℝ)

High correlation  Missing 

Distinct2262
Distinct (%)66.7%
Missing4055
Missing (%)54.5%
Infinite0
Infinite (%)0.0%
Mean0.0094342742
Minimum0.0062750001
Maximum0.013043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.2 KiB
2025-01-09T13:44:08.001854image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.0062750001
5-th percentile0.0072621001
Q10.00832775
median0.0092150001
Q30.010147
95-th percentile0.01244845
Maximum0.013043
Range0.0067680003
Interquartile range (IQR)0.0018192497

Descriptive statistics

Standard deviation0.0015522421
Coefficient of variation (CV)0.16453222
Kurtosis-0.38771051
Mean0.0094342742
Median Absolute Deviation (MAD)0.00090400036
Skewness0.58676487
Sum32.001058
Variance2.4094556 × 10-6
MonotonicityNot monotonic
2025-01-09T13:44:08.103454image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.009235999547 7
 
0.1%
0.008039000444 7
 
0.1%
0.009823000059 7
 
0.1%
0.009867999703 5
 
0.1%
0.009800000116 5
 
0.1%
0.009224000387 5
 
0.1%
0.008298000321 5
 
0.1%
0.009243999608 5
 
0.1%
0.009309000336 5
 
0.1%
0.008484999649 5
 
0.1%
Other values (2252) 3336
44.8%
(Missing) 4055
54.5%
ValueCountFrequency (%)
0.006275000051 1
< 0.1%
0.006283999886 1
< 0.1%
0.006293000188 1
< 0.1%
0.006298999768 2
< 0.1%
0.006304999813 1
< 0.1%
0.006306000054 1
< 0.1%
0.006318000145 1
< 0.1%
0.006322000176 1
< 0.1%
0.006335999817 1
< 0.1%
0.006341999862 1
< 0.1%
ValueCountFrequency (%)
0.01304300036 1
< 0.1%
0.01303999964 1
< 0.1%
0.01303599961 1
< 0.1%
0.01303400006 2
< 0.1%
0.01303100027 1
< 0.1%
0.01302400045 1
< 0.1%
0.01302099973 1
< 0.1%
0.01301799994 1
< 0.1%
0.01301100012 2
< 0.1%
0.01300899964 1
< 0.1%

root.sg1.device1.projetv7.G
Real number (ℝ)

High correlation  Missing 

Distinct2385
Distinct (%)74.7%
Missing4256
Missing (%)57.2%
Infinite0
Infinite (%)0.0%
Mean0.66191522
Minimum0.52383399
Maximum0.83255601
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.2 KiB
2025-01-09T13:44:08.201956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.52383399
5-th percentile0.54888549
Q10.590249
median0.64176601
Q30.72453251
95-th percentile0.80218852
Maximum0.83255601
Range0.30872202
Interquartile range (IQR)0.13428351

Descriptive statistics

Standard deviation0.082406938
Coefficient of variation (CV)0.12449772
Kurtosis-1.1366421
Mean0.66191522
Median Absolute Deviation (MAD)0.064947963
Skewness0.331119
Sum2112.1715
Variance0.0067909039
MonotonicityNot monotonic
2025-01-09T13:44:08.298316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7097229958 12
 
0.2%
0.7099750042 11
 
0.1%
0.7102270126 10
 
0.1%
0.7094709873 9
 
0.1%
0.7079650164 8
 
0.1%
0.706215024 8
 
0.1%
0.6075329781 8
 
0.1%
0.532622993 8
 
0.1%
0.5333330035 7
 
0.1%
0.7935879827 6
 
0.1%
Other values (2375) 3104
41.7%
(Missing) 4256
57.2%
ValueCountFrequency (%)
0.5238339901 1
< 0.1%
0.5239719748 1
< 0.1%
0.5254859924 1
< 0.1%
0.5261769891 1
< 0.1%
0.5263159871 1
< 0.1%
0.5274260044 1
< 0.1%
0.5275650024 2
< 0.1%
0.5281230211 2
< 0.1%
0.5291010141 1
< 0.1%
0.5293809772 1
< 0.1%
ValueCountFrequency (%)
0.8325560093 1
< 0.1%
0.8313239813 1
< 0.1%
0.8308060169 1
< 0.1%
0.8258320093 1
< 0.1%
0.8245720267 1
< 0.1%
0.8244019747 2
< 0.1%
0.8230450153 1
< 0.1%
0.8228420019 1
< 0.1%
0.8218280077 1
< 0.1%
0.8213919997 1
< 0.1%

Interactions

2025-01-09T13:44:05.334523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:57.063887image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:58.303229image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:59.613446image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:00.812038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:01.973277image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:03.141965image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:03.926981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:04.681637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:05.405124image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:57.227477image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:58.442022image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:59.744239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:00.947845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:02.113618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:03.244017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:04.119820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:04.756239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:05.476284image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:57.369364image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:58.576006image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:59.885975image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:01.075088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:02.248862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:03.339978image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:04.198122image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:04.829057image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:05.549893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:57.497490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:58.723289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:59.993808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:01.215260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:02.396827image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:03.424241image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:04.275784image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:04.908027image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:05.612531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:57.625578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:58.841773image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:00.126550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:01.331083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:02.522112image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:03.505294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:04.337302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:04.974141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:05.680449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:57.766823image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:58.980067image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:00.269432image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:01.465578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:02.662958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:03.596530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:04.407445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:05.049368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:05.749469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:57.896738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:59.110771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:00.391884image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:01.591169image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:02.793902image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:03.679299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:04.474716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:05.119995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:05.815349image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:58.027763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:59.346078image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:00.523887image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:01.711762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:02.912700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:03.767328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:04.542621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:05.191207image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:05.890316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:58.171576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:43:59.482378image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:00.672771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:01.849022image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:03.038245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:03.854546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:04.614680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-09T13:44:05.265536image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-09T13:44:08.367119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Timeroot.sg1.device1.projetv7.Aroot.sg1.device1.projetv7.Broot.sg1.device1.projetv7.Croot.sg1.device1.projetv7.Droot.sg1.device1.projetv7.Eroot.sg1.device1.projetv7.Froot.sg1.device1.projetv7.Groot.sg1.device1.projetv7.OT
Time1.0000.524-0.2360.4050.7710.3290.5450.7010.677
root.sg1.device1.projetv7.A0.5241.0000.1490.8900.7420.4170.4890.7190.887
root.sg1.device1.projetv7.B-0.2360.1491.0000.229-0.0090.015-0.264-0.1240.120
root.sg1.device1.projetv7.C0.4050.8900.2291.0000.6800.6310.4260.5310.708
root.sg1.device1.projetv7.D0.7710.742-0.0090.6801.0000.3370.6820.8690.890
root.sg1.device1.projetv7.E0.3290.4170.0150.6310.3371.000-0.0390.1660.234
root.sg1.device1.projetv7.F0.5450.489-0.2640.4260.682-0.0391.0000.7290.610
root.sg1.device1.projetv7.G0.7010.719-0.1240.5310.8690.1660.7291.0000.755
root.sg1.device1.projetv7.OT0.6770.8870.1200.7080.8900.2340.6100.7551.000

Missing values

2025-01-09T13:44:05.979249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-09T13:44:06.097214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-09T13:44:06.227985image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Timeroot.sg1.device1.projetv7.Aroot.sg1.device1.projetv7.Broot.sg1.device1.projetv7.Croot.sg1.device1.projetv7.Droot.sg1.device1.projetv7.OTroot.sg1.device1.projetv7.Eroot.sg1.device1.projetv7.Froot.sg1.device1.projetv7.G
06311484000000.78551.61100.8616980.634196NaN0.2112420.0068380.525486
16312348000000.78181.61000.8611040.633513NaN0.2112420.0068630.523972
26313212000000.78671.62930.8610300.648508NaN0.2112420.0069750.526316
36314076000000.78601.63700.8620690.650618NaN0.2112420.0069530.523834
46314940000000.78491.65300.8619950.656254NaNNaN0.0069400.527426
56315804000000.78661.65370.8610300.654879NaNNaN0.0068870.526177
66316668000000.78861.66200.8628870.661157NaNNaN0.0068850.527565
76317532000000.79101.65680.8643040.659631NaNNaN0.0068780.527565
86318396000000.79391.66950.8642300.669120NaNNaN0.0068780.528123
96319260000000.78941.65700.8583690.659413NaNNaN0.0068740.528123
Timeroot.sg1.device1.projetv7.Aroot.sg1.device1.projetv7.Broot.sg1.device1.projetv7.Croot.sg1.device1.projetv7.Droot.sg1.device1.projetv7.OTroot.sg1.device1.projetv7.Eroot.sg1.device1.projetv7.Froot.sg1.device1.projetv7.G
743712772440000000.7470771.3204810.759425NaN0.7263480.1499210.009742NaN
743812773304000000.7475801.3227510.758593NaN0.7290760.1499900.0097660.733703
743912774168000000.7509761.3224010.761058NaN0.7310740.1498680.0098020.733579
744012775032000000.7485591.3025920.756885NaN0.7264010.1497840.0097730.730762
744112775896000000.7492881.3000010.756796NaN0.7265590.1485800.0097770.730946
744212776760000000.7491761.3005000.756802NaN0.7264010.1498510.0097780.730941
744312777624000000.7542901.3029320.757579NaN0.7300070.1500040.0098300.734498
744412778488000000.7542051.2973530.756024NaN0.7307270.1498130.0098230.733708
744512779352000000.7580061.2995450.757581NaN0.7316090.1498760.0099140.736223
744612780216000000.7640001.3094150.766163NaN0.7309940.1500080.0099210.737300

Validity Analysis

Column: Time

{'min_value': np.int64(631148400000), 'max_value': np.int64(1278021600000), 'out_of_range_count': 7447}

Consistency Analysis

Number of duplicate rows: 0

Column Time: 7447 unique values

Column root.sg1.device1.projetv7.A: 2670 unique values

Column root.sg1.device1.projetv7.B: 2789 unique values

Column root.sg1.device1.projetv7.C: 2737 unique values

Column root.sg1.device1.projetv7.D: 2761 unique values

Column root.sg1.device1.projetv7.OT: 2585 unique values

Column root.sg1.device1.projetv7.E: 1616 unique values

Column root.sg1.device1.projetv7.F: 2262 unique values

Column root.sg1.device1.projetv7.G: 2385 unique values